Learn why MongoDB is spreading like wildfire across capital markets (and really every industry) and then focus in particular on how financial firms are enjoying the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data.

1. How Capital Markets Firms Use
MongoDB as a Tick Database
Antoine Girbal, Technical Account Manager
Email: antoine@10gen.com
Twitter: @antoinegirbal

2. 2
• MongoDB Introduction
• FS Use Cases
• Writing/Capturing Market Data
• Reading/Analyzing Market Data
• Performance, Scalability, & High Availability
• Q&A
Agenda

3. 3
Introduction
10gen is the company behind MongoDB –
the leading next generation database
Document-
Oriented
Open-
Source
General
Purpose

4. 4
10gen Overview
200+ employees 500+ customers
Over $81 million in funding
Offices in New York, Palo Alto, Washington
DC, London, Dublin, Barcelona and Sydney

5. 5
Database Landscape
• No Automatic Joins
• Document Transactions
• Fast, Scalable Read/Writes

6. 6
MongoDB Business Benefits
Increased Developer Productivity Better Customer Experience
Faster Time to Market Lower TCO

7. 7
MongoDB Technical Benefits
Horizontally Scalable
-Sharding
Agile &
Flexible
High
Performance
-Indexes
-RAM
Application
Highly
Available
-Replica Sets
{ author: “roger”,
date: new Date(),
text: “Spirited Away”,
tags: [“Tezuka”, “Manga”]}

8. 8
Most Common FS Use Cases
1. Tick Data Capture & Analysis
2. Reference Data Management
3. Risk Analysis & Reporting
4. Trade Repository
5. Portfolio Reporting

9. 9
Tick Data Capture & Analysis -
Requirements
• Capture real-time market data (multi-asset, top of
book, depth of book, even news)
• Load historical data
• Aggregate data into bars, daily, monthly intervals
• Enable queries & analysis on raw ticks or
aggregates
• Drive backtesting or automated signals

10. 10
Tick Data Capture & Analysis –
Why MongoDB?
• High throughput => can capture real-time feeds for all
products/asset classes needed
• High scalability => all data and depth for all historical time periods
can be captured
• Flexible & Range-based indexing => fast querying on time ranges
and any fields
• Aggregation Framework => can shape raw data into aggregates
(e.g. ticks to bars)
• Map-reduce capability (Native MR or Hadoop Connector) => batch
analysis looking for patterns and opportunities
• Easy to use => native language drivers and JSON expressions that
you can apply for most operational database needs as well
• Low TCO => Low software license cost and commodity hardware

11. Writing/Capturing Tick Data

12. 12
Trades/metrics
High Level Trading Architecture
Feed Handler
Exchanges/Mark
ets/Brokers
Capturing
Application
Low Latency
Applications
Higher Latency
Trading
Applications
Backtesting and
Analysis
Applications
Market Data
Cached Static &
Aggregated Data
News & social
networking
sources
Orders
Orders

13. 13
Trades/metrics
High Level Trading Architecture
Feed Handler
Exchanges/Mark
ets/Brokers
Capturing
Application
Low Latency
Applications
Higher Latency
Trading
Applications
Backtesting and
Analysis
Applications
Market Data
Cached Static &
Aggregated Data
News & social
networking
sources
Orders
Orders
Data Types
• Top of book
• Depth of book
• Multi-asset
• Derivatives (e.g. strips)
• News (text, video)
• Social Networking

14. 14
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bidPrice: 55.37,
offerPrice: 55.58,
bidQuantity: 500,
offerQuantity: 700
}
> db.ticks.find( {symbol: "DIS",
bidPrice: {$gt: 55.36} } )
Top of book [e.g. equities]

15. 15
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bidPrices: [55.37, 55.36, 55.35],
offerPrices: [55.58, 55.59, 55.60],
bidQuantities: [500, 1000, 2000],
offerQuantities: [1000, 2000, 3000]
}
> db.ticks.find( {bidPrices: {$gt: 55.36} } )
Depth of book

16. 16
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bids: [
{price: 55.37, amount: 500},
{price: 55.37, amount: 1000},
{price: 55.37, amount: 2000} ],
offers: [
{price: 55.58, amount: 1000},
{price: 55.58, amount: 2000},
{price: 55.59, amount: 3000} ]
}
> db.ticks.find( {"bids.price": {$gt: 55.36} } )
or any way your app uses it

17. 17
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
spreadPrice: 0.58
leg1: {symbol: “CLM13”, price: 97.34}
leg2: {symbol: “CLK13”, price: 96.92}
}
db.ticks.find( { leg1 : “CLM13” },
{ leg2 : “CLK13” },
{ spreadPrice : {$gt: 0.50 } } )
Synthetic spreads

18. 18
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
title: “Disney Earnings…”
body: “Walt Disney Company reported…”,
tags: [“earnings”, “media”, “walt disney”]
}
News

19. 19
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
timestamp: ISODate("2013-02-15 10:00"),
twitterHandle: “jdoe”,
tweet: “Heard @DisneyPictures is releasing…”,
usernamesIncluded: [“DisneyPictures”],
hashTags: [“movierumors”, “disney”]
}
Social networking

20. 20
{
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS”,
openTS: Date("2013-02-15 10:00"),
closeTS: Date("2013-02-15 10:05"),
open: 55.36,
high: 55.80,
low: 55.20,
close: 55.70
}
Aggregates (bars, daily, etc.)

21. Querying/Analyzing Tick Data

22. 22
Architecture for Querying Data
Higher Latency
Trading
Applications
Backtesting
Applications
• Ticks
• Bars
• Other analysis
Research &
Analysis
Applications

23. 23
Index any fields: arrays, nested, etc
// Compound indexes
> db.ticks.ensureIndex({symbol: 1, timestamp:1})
// Index on arrays
>db.ticks.ensureIndex( {bidPrices: -1})
// Index on any depth
> db.ticks.ensureIndex( {“bids.price”: 1} )
// Full text search
> db.ticks.ensureIndex ( {tweet: “text”} )

24. 24
Query for ticks by time; price
threshold
// Ticks for last month for media companies
> db.ticks.find({
symbol: {$in: ["DIS", “VIA“, “CBS"]},
timestamp: {$gt: new ISODate("2013-01-01")},
timestamp: {$lte: new ISODate("2013-01-31")}})
// Ticks when Disney’s bid breached 55.50 this month
> db.ticks.find({
symbol: "DIS",
bidPrice: {$gt: 55.50},
timestamp: {$gt: new ISODate("2013-02-01")}})

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• Custom application code
– Run your queries, compute your results
• Aggregation framework
– Declarative, pipeline-based approach
• Native Map/Reduce in MongoDB
– Javascript functions distributed across cluster
• Hadoop Connector
– Offline batch processing/computation
Analyzing/Aggregating Options

26. 26
//Aggregate minute bars for Disney for this month
db.ticks.aggregate(
{ $match: {symbol: "DIS”, timestamp: {$gt: new ISODate("2013-02-01")}}},
{ $project: {
year: {$year: "$timestamp"},
month: {$month: "$timestamp"},
day: {$dayOfMonth: "$timestamp"},
hour: {$hour: "$timestamp"},
minute: {$minute: "$timestamp"},
second: {$second: "$timestamp"},
timestamp: 1,
price: 1}},
{ $sort: { timestamp: 1}},
{ $group :
{ _id : {year: "$year", month: "$month", day: "$day", hour: "$hour", minute: "$minute"},
open: {$first: "$price"},
high: {$max: "$price"},
low: {$min: "$price"},
close: {$last: "$price"} }} )
Aggregate into min bars

27. 27
…
//then count the number of down bars
{ $project: {
downBar: {$lt: [“$close”, “$open”] },
timestamp: 1,
open: 1, high: 1, low: 1, close: 1}},
{ $group: {
_id: “$downBar”,
sum: {$sum: 1}}} })
Add analysis on the bars

28. 28
var mapFunction = function () {
emit(this.symbol, this.bidPrice);
}
var reduceFunction = function (symbol, priceList) {
return Array.sum(priceList);
}
> db.ticks.mapReduce(
map, reduceFunction, {out: ”tickSums"})
Map-Reduce Example: Sum

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• MongoDB’s Hadoop Connector
• Supports Map/Reduce, Streaming, Pig
• MongoDB as input/output storage for Hadoop
jobs
– No need to go through HDFS
• Leverage power of Hadoop ecosystem against
operational data in MongoDB
Process Data on Hadoop

30. Performance, Scalability, and
High Availability

31. 31
Why MongoDB is fast and scalable
Better data locality
Relational MongoDB
In-Memory
Caching
Auto-Sharding
Read/write scaling

32. 32
Auto-Sharding for Horizontal Scale
mongod
Read/Write Scalability
Key Range
Symbol: A…Z

33. 33
Auto-Sharding for Horizontal Scale
Read/Write Scalability
mongod mongod
Key Range
Symbol: A…J
Key Range
Symbol: K…Z

34. 34
Sharding
mongod mongod
mongod mongod
Read/Write Scalability
Key Range
Symbol: A…F
Key Range
Symbol: G…J
Key Range
Symbol: K…O
Key Range
Symbol: P…Z

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Primary
Secondary
Secondary
Primary
Secondary
Secondary
Primary
Secondary
Secondary
Primary
Secondary
Secondary
MongoS MongoS MongoS
Application
Key Range
Symbol: A…F,
Time
Key Range
Symbol: G…J,
Time
Key Range
Symbol: K…O,
Time
Key Range
Symbol: P…Z,
Time

36. 36
Subscriptions
Professional Support, Enterprise Edition and Commercial License
10gen Products and Services
Consulting
Expert Resources for All Phases of MongoDB Implementations
Training
Online and In-Person, for Developers and Administrators

37. 37
• MongoDB is high performance for tick data
• Scales horizontally automatically by auto-
sharding
• Fast, flexible querying, analysis, & aggregation
• Dynamic schema can handle any data types
• MongoDB has all these features with low TCO
• 10gen can support you with anything discussed
Summary

38. 38
Resource Location
MongoDB Downloads www.mongodb.org/download
Free Online Training education.10gen.com
Webinars and Events www.10gen.com/events
White Papers www.10gen.com/white-papers
Customer Case Studies www.10gen.com/customers
Presentations www.10gen.com/presentations
Documentation docs.mongodb.org
Additional Info info@10gen.com
For More Information
Resource User Data Management

39. How Capital Markets Firms Use
MongoDB as a Tick Database
Matt Kalan, Sr. Solution Architect
Email: Matt.kalan@10gen.com
Twitter: @matthewkalan